






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   __A					=	argparse.ArgumentParser(
       description=(
           "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
           " Distillation"
       )
   )
   parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
   parser.add_argument("--model_name", default="roberta-large", type=str)
   parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
   parser.add_argument("--vocab_transform", action="store_true")
   __A					=	parser.parse_args()

   if args.model_type == "roberta":
      __A					=	RobertaForMaskedLM.from_pretrained(args.model_name)
      __A					=	"roberta"
   elif args.model_type == "gpt2":
      __A					=	GPTaLMHeadModel.from_pretrained(args.model_name)
      __A					=	"transformer"

   __A					=	model.state_dict()
   __A					=	{}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         __A					=	state_dict[F"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         __A					=	F"""{prefix}.embeddings.{w}.weight"""
         __A					=	state_dict[param_name]
      for w in ["weight", "bias"]:
         __A					=	F"""{prefix}.embeddings.LayerNorm.{w}"""
         __A					=	state_dict[param_name]

    # Transformer Blocks #
   __A					=	0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               __A					=	state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         __A					=	state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               __A					=	state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         __A					=	state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            __A					=	state_dict[F"""lm_head.dense.{w}"""]
            __A					=	state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         __A					=	state_dict[F"""{prefix}.ln_f.{w}"""]
      __A					=	state_dict["lm_head.weight"]

   print(F"""N layers selected for distillation: {std_idx}""")
   print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)






"""simple docstring"""



import math




class 		a_    :







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							list[list[float]]  ,       __UpperCamelCase      :							list[int]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = 0.0
        _UpperCAmelCase        = 0.0
        for i in range(len(__UpperCamelCase  )  ):
            da += math.pow((sample[i] - weights[0][i])  ,       2  )
            da += math.pow((sample[i] - weights[1][i])  ,       2  )
            return 0 if da > da else 1
        return 0







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							list[list[int | float]]  ,       __UpperCamelCase      :							list[int]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							float  )		->list[list[int | float]]:






        '''simple docstring'''






        for i in range(len(__UpperCamelCase  )  ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights







def 			_UpperCamelCase						(    )     ->	None:





    """simple docstring"""

    _UpperCAmelCase        = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    _UpperCAmelCase        = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    _UpperCAmelCase        = SelfOrganizingMap()
    _UpperCAmelCase        = 3
    _UpperCAmelCase        = 0.5

    for _ in range(_A   ):
        for j in range(len(_A   )   ):
            # training sample
            _UpperCAmelCase        = training_samples[j]

            # Compute the winning vector
            _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

            # Update the winning vector
            _UpperCAmelCase        = self_organizing_map.update(_A       ,   _A       ,   _A       ,   _A   )

    # classify test sample
    _UpperCAmelCase        = [0, 0, 0, 1]
    _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""   )
    print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
 main()